Adaptive and optimal imaging of quantum optical systems for quantum computing

ABSTRACT

The disclosure describes an adaptive and optimal imaging of individual quantum emitters within a lattice or optical field of view for quantum computing. Advanced image processing techniques are described to identify individual optically active quantum bits (qubits) with an imager. Images of individual and optically-resolved quantum emitters fluorescing as a lattice are decomposed and recognized based on fluorescence. Expected spatial distributions of the quantum emitters guides the processing, which uses adaptive fitting of peak distribution functions to determine the number of quantum emitters in real time. These techniques can be used for the loading process, where atoms or ions enter the trap one-by-one, for the identification of solid-state emitters, and for internal state-detection of the quantum emitters, where each emitter can be fluorescent or dark depending on its internal state. This latter application is relevant to efficient and fast detection of optically active qubits in quantum simulations and quantum computing.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a divisional application of U.S. patentapplication Ser. No. 16/239,084 filed on Jan. 3, 2019, and entitled“ADAPTIVE AND OPTIMAL IMAGING OF QUANTUM OPTICAL SYSTEMS FOR QUANTUMCOMPUTING,” which claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/614,108, entitled “ADAPTIVE AND OPTIMALIMAGING OF QUANTUM OPTICAL SYSTEM FOR QUANTUM COMPUTING,” and filed onJan. 5, 2018, the contents of each of which are incorporated herein byreference in their entirety.

GOVERNMENT LICENSE RIGHTS

This invention was made with government support under H9823015CO345awarded by the MPO, W911NF1410599 awarded by the Army Research Office(ARO), and W911NF1610082 awarded by the Intelligence Advanced ResearchProjects Activity (IARPA). The government has certain rights in theinvention.

BACKGROUND OF THE DISCLOSURE

Aspects of the present disclosure generally relate to quantum computingsystems, and more specifically, to adaptive and optimal imaging ofquantum optical systems for quantum computing.

Individual optically-active quantum systems such as trapped atoms areone of the leading implementations for quantum information processing.Atomic-based qubits can be used as quantum memories, can host quantumgates in quantum computers and simulators, and can act as nodes forquantum communication networks. Qubits based on trapped atomic ionsenjoy a rare combination of attributes. For example, qubits based ontrapped atomic ions have very good coherence properties, can be preparedand measured with nearly 100% efficiency, and are readily entangled witheach other by modulating their Coulomb interaction or remote photonicinterconnects. Lattice of cold (e.g., laser-cooled) trapped atoms havealso proven useful for precision metrology, including sensors of smallforces and atomic clocks.

Accurate and controlled placement and number of quantum systems such astrapped atomic ions is critical in the operation of systems that providequantum information processing and in the ability of such systems to beconfigurable. Accordingly, imaging devices and image processingalgorithms are needed to ensure that the placement, number, and/or stateof quantum systems such as atomic ions is appropriate for properoperation.

Therefore, techniques that allow for adaptive and optimal imaging ofindividual optically-active quantum systems such as trapped atoms, ions,or other optically active quantum system within a lattice are desirable.

SUMMARY OF THE DISCLOSURE

The following presents a simplified summary of one or more aspects inorder to provide a basic understanding of such aspects. This summary isnot an extensive overview of all contemplated aspects, and is intendedto neither identify key or critical elements of all aspects nordelineate the scope of any or all aspects. Its purpose is to presentsome concepts of one or more aspects in a simplified form as a preludeto the more detailed description that is presented later.

In an aspect of the disclosure, various techniques are described forimaging quantum systems such as individual quantum emitters (e.g.,atoms, ions, solid-state quantum emitters such as quantum dotes( )basedon their fluorescence, allowing the fast and accurate determination ofthe number of quantum emitters or equivalent quantum systems fluorescingin the trap. As part of these techniques, image processing algorithmsare described to adaptively determine the positions of individualquantum emitters immersed in a lattice or in a field of view, for boththe identification of quantum emitters during the loading procedure, forthe identification of solid-state emitters, and also for thehigh-efficiency detection of the qubit state of each quantum emitter,for example at the conclusion of a quantum computation.

In an aspect of this disclosure, a method for identification ofoptically active quantum systems that include one or more individualquantum emitters is described that includes providing an optical sourcethat produces fluorescence from the quantum emitters as they are loadedinto a trap, each of the quantum emitters behaving as an optical objecthaving a certain intensity distribution in response to the fluorescence,identifying a position of each of the quantum emitters by fitting theoverall intensity distribution to a sum of a variable number of Gaussianfunctions, and controlling, in real-time, a number of quantum emittersthat are loaded into the trap based at least on the identified positionsof each of the quantum emitters and whether one or more of the quantumemitters are not fluorescing.

In another aspect of this disclosure, a method for identification ofquantum emitters is described that includes preparing, for each ofmultiple trapped quantum emitters, a particular quantum state thatfluoresces while keeping all other quantum emitters in a differentquantum state to establish an individual basis intensity distributionfor each of the multiple trapped quantum emitters, determining a peakintensity for the multiple trapped quantum emitters in a particularquantum state that fluoresces, performing a maximum likelihood method todecompose a distribution of the peak intensities into a best fit linearcombination of all of the individual basis intensity distribution, andidentifying a qubit value for the multiple trapped quantum emittersbased on results from the maximum likelihood method decomposition.

In another aspect of this disclosure, a quantum information processing(QIP) system is described that includes a quantum emitter lattice, anoptical controller configured to provide an optical source that producesfluorescence on the quantum emitters as they are loaded into thelattice, each of the quantum emitters behaving as a point-source opticalobject having a certain intensity in response to the fluorescence, andan imaging system configured to identify a position of each of thequantum emitters by fitting the overall intensity distribution to a sumof a variable number of Gaussian function, and control, in real-time, anumber of quantum emitters that are loaded into the lattice based atleast on the identified positions of each of the quantum emitters andwhether any of the atoms is not fluorescing.

In another aspect of this disclosure, a QIP system is described thatincludes a quantum emitter lattice, an optical controller configured toprepare, for each of multiple quantum emitters trapped in the lattice, abright qubit state while keeping all other quantum emitters in a darkqubit state to establish an individual basis intensity distribution foreach of the multiple trapped quantum emitters, and an imaging systemconfigured to determine a peak intensity for the multiple trappedquantum emitters in a bright qubit state, perform a maximum likelihoodmethod to decompose a distribution of the peak intensities into a bestfit linear combination of all of the individual basis intensitydistributions, and identify a qubit value for the multiple trappedquantum emitters based on results from the maximum likelihood methoddecomposition.

In another aspect of this disclosure, a computer-readable medium storingcode with instructions executable by a processor for identification ofquantum emitters is described that includes code for providing anoptical source that produces fluorescence on the quantum emitters asthey are loaded into a lattice, each of the quantum emitters behaving asa point-source optical object having a certain intensity in response tothe fluorescence, code for identifying a position of each of the quantumemitters by fitting the overall intensity distribution to a sum of avariable number of Gaussian functions, and code for controlling, inreal-time, a number of quantum emitters that are loaded into the latticebased at least on the identified positions of each of the quantumemitters and whether any of the quantum emitters is not fluorescing.

In another aspect of this disclosure, a computer-readable medium storingcode with instructions executable by a processor for identification ofquantum emitters is described that include code for preparing, for eachof multiple trapped quantum emitters, a bright qubit state while keepingall other quantum emitters in a dark qubit state to establish anindividual basis intensity distribution for each of the multiple trappedquantum emitters, code for determining a peak intensity for the multipletrapped quantum emitters in a bright qubit state, code for performing amaximum likelihood method to decompose a distribution of the peakintensities into a best fit linear combination of all of the individualbasis intensity distributions, and code for identifying a qubit valuefor the multiple trapped quantum emitters based on results from themaximum likelihood method decomposition.

In yet another aspect of this disclosure, a method for identification ofoptically active quantum systems that include one or more individualquantum emitters is described where the method includes providing anoptical source that produces fluorescence from the quantum emitterswithin an optical field of view, each of the quantum emitters behavingas an optical object having a certain intensity distribution in responseto the fluorescence, identifying a position of each of the quantumemitters by fitting the overall intensity distribution to a sum of avariable number of Gaussian functions, and controlling, in real-time, anumber of quantum emitters that are within the field of view based atleast on the identified positions of each of the quantum emitters andwhether one or more of the quantum emitters are not fluorescing.

Described herein are methods, apparatuses, and computer-readable storagemedium for various aspects associated with adaptive and optimal imagingof quantum optical systems for quantum computing. These techniques mayalso be useful for quantum metrology and sensing applications.

BRIEF DESCRIPTION OF THE DRAWINGS

The appended drawings illustrate only some implementation and aretherefore not to be considered limiting of scope.

FIG. 1A illustrates a view of a vacuum chamber that houses electrodesfor the trapping of atomic ions a linear lattice in accordance withaspects of the disclosure.

FIG. 1B is a diagram illustrating an example of a reduced energy leveldiagram showing the application of laser radiation for stateinitialization in accordance with aspects of the disclosure.

FIG. 1C is a diagram illustrating an example of a reduced energy leveldiagram showing the application of laser radiation for qubit statedetection through fluorescence in accordance with aspects of thedisclosure.

FIGS. 2A and 2B are images that illustrate examples of identificationand labeling of fluorescing atoms using imaging techniques in accordancewith an aspect of the disclosure.

FIG. 2C is a diagram illustrating an example of the type ofidentification provided by the imaging techniques described inaccordance with an aspect of the disclosure.

FIG. 3 is a diagram that illustrates an example of a computer device inaccordance with aspects of this disclosure.

FIG. 4A is a flow diagram that illustrates an example of a method inaccordance with aspects of this disclosure.

FIG. 4B is a flow diagram that illustrates an example of another methodin accordance with aspects of this disclosure.

FIG. 5 is a flow diagram that illustrates an example of another methodin accordance with aspects of this disclosure.

FIG. 6A is a block diagram that illustrates an example of a quantuminformation processing (QIP) system in accordance with aspects of thisdisclosure.

FIG. 6B is a block diagram that illustrates an example of an imagingsystem in accordance with aspects of this disclosure.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of various configurations and isnot intended to represent the only configurations in which the conceptsdescribed herein may be practiced. The detailed description includesspecific details for the purpose of providing a thorough understandingof various concepts. However, it will be apparent to those skilled inthe art that these concepts may be practiced without these specificdetails. In some instances, well known components are shown in blockdiagram form in order to avoid obscuring such concepts.

As described above, trapped atoms may be used to implement quantuminformation processing. Atomic-based qubits can be used as differenttype of devices, including but not limited to quantum memories, the hostof quantum gates in quantum computers and simulators, and nodes forquantum communication networks. Qubits based on trapped atomic ions(atoms with a net state of electrical charge) can have very goodcoherence properties, can be prepared and measured with nearly 100%efficiency, and can be readily entangled with each other by modulatingtheir Coulomb interaction or remote photonic interconnects. Lattices ofcold (e.g., laser-cooled) trapped atoms have also proven useful forprecision metrology, including sensors of small forces and atomicclocks.

In addition to trapped atoms or ions, quantum information processing canbe performed using solid-state qubits, which like some atoms or ions canbe optically active. Solid-state qubits are locked in as part of thefundamental atomic lattice making up the solid; they are usually singleisolated defects or grain boundaries that make an effective“atomic-like” system inside the solid and therefore in this instancesthere is not trap to contain the solid-state qubits. However, thesetypes of qubits can diffuse around a bit, they can change character orproperties with electrical strain or other types of fields, and canblink off or bleach when illuminated by light. Therefore, solid-statequbits or solid-state quantum emitters have many of the same featuresthat are used for imaging trapped ions and, consequently, many of theimaging aspects described herein that are applicable to trapped atoms orions may also be applicable to the imaging of solid-state qubits in afield of view.

As used in this disclosure, the terms “atoms,” “atomic ions,” and “ions”may be used interchangeably to describe the particles that are isolatedand controlled or are actually confined in a trap to form a lattice orsimilar arrangement or configuration. Moreover, for optically-activesystems or optically-active quantum bits, the term “quantum emitter” maybe used to refer to an “atom,” “atomic ion,” “ion”, “solid-state quantumemitter”, “quantum dot” or some other system that can be made to emitradiation that can be imaged or detected. Where the charge state of theatom (neutral atom or any charge state of the atomic ion) is notrelevant, the disclosure described techniques that can be used for anytype of neutral atom or atomic ion or other type of optically activequantum system. This disclosure describes techniques in the form ofmethods or processes and equipment or apparatuses for imaging trappedatoms based on their fluorescence. These techniques can be used for boththe identification of atomic ions during the loading procedure and alsofor the high-efficiency detection of the qubit state of each atomic ion,for example at the conclusion of a quantum computation.

In the case of atomic ions, the typical ion trap geometry or structureused for quantum information and metrology purposes is the linear radiofrequency (RF) Paul trap (also referred to as an RF trap or simply aPaul trap), where nearby electrodes hold static and dynamic electricalpotentials that lead to an effective inhomogeneous harmonic confinementof the ions. The RF Paul trap is a type of trap that uses electricfields to trap or confine charged particles in a particular region,position, or location. When atomic ions are laser-cooled to very lowtemperatures in such a trap, the atomic ions form a stationary latticeof qubits (e.g., a structured arrangement of qubits), with Coulombrepulsion balancing the external confinement force. For sufficient trapanisotropy, the ions can form a linear lattice along the weak directionof confinement, and this is the arrangement typically employed forapplications in quantum information and metrology. As the trapanisotropy is reduced, the atomic ions undergo a series of phasetransitions in their static conformation in space, evolving to atwo-dimensional (2D) zig-zag or jagged type structure, then athree-dimensional (3D) helical structure, ultimately toward a sphericallattice when the three directions of confinement approach isotropy.

FIG. 1A illustrates a partial view of a vacuum chamber 100 that houseselectrodes for the trapping of atomic ions in a linear lattice 110 usinga linear RF Paul trap. In the example shown in FIG. 1A, a vacuum chamberin a quantum system includes electrodes for trapping 20 atomic Ytterbiumions (e.g., ¹⁷¹Yb⁺ ions) which are confined in the linear lattice 110and are laser-cooled to be nearly at rest. While 20 atomic ions areshown in this example, the number of atomic ions trapped can beconfigurable and more or fewer than 20 atomic ions may be trapped. Theatoms are illuminated with laser radiation tuned to a resonance in¹⁷¹Yb⁺ and the fluorescence of the atomic ions is imaged onto a camera.In this example, atomic ions are separated by a distance 115 that isabout 5 microns (μm) from each other as shown by fluorescence. Theseparation of the atomic ions is determined by a balance between theexternal confinement force and Coulomb repulsion.

Atomic ions are typically loaded into traps by creating a neutral atomicflux of the desired particle, and ionizing them once in the trappingvolume. Ions can remain confined for months, with lifetimes oftenlimited by the level of vacuum. Elastic collisions with residualbackground gas occur roughly once per hour per ion at typical ultra-highvacuum (UHV) pressures (˜10⁻¹¹ torr) and do not necessarily eject theion from its position in the trap, although inelastic collisions canchange the species or isotope of the trapped ion. Cryogenic chambers canvirtually eliminate these collision events by further reducing thebackground pressure (e.g., limiting the outgassing of materials).

Strong fluorescence of individual trapped atomic ions relies on theefficient cycling of photons, thus the atomic structure of the ion musthave a strong closed optical transition that allows for laser-cooling ofthe motion, qubit state initialization, and efficient qubit readout.This may rule out many atomic ion species, apart from simple atomic ionswith a lone outer electron, such as the alkaline-earths (Be⁺, Mg⁺, Ca⁺,Sr⁺, Ba⁺) and particular transition metals (Zn⁺, Hg⁺, Cd⁺, and Yb⁺).Within these atomic ions, quantum bits can be represented by two stableelectronic levels, often characterized by an effective spin with the twostates |↑

and |↓

, or equivalently |1

and |0

. FIG. 1B and FIG. 1C show the reduced energy level diagrams 120 and150, respectively, for atomic ion ¹⁷¹Yb⁺ where the qubit levels |↑

and |↓

130 are represented by the stable hyperfine levels in the groundelectronic state, and are separated by frequency near ω₀/2π=12.642 GHz.The excited electronic states |e

and |e′

140 in ¹⁷¹YB⁺ are themselves split by a smaller hyperfine coupling andare separated from the ground states by an optical interval having anenergy corresponding to an optical wavelength of 369.53 nm.

Laser radiation tuned just below resonance in these optical transitionsallows for Doppler laser cooling to confine the atomic ions near thebottom of the trap. Other more sophisticated forms of laser cooling canbring the atomic ions to be nearly at rest in the trap.

When a bichromatic laser beam (e.g., a beam with two tones produced bysidebands resulting from optical modulation) resonant with both |↑

↔|↓

↔|e′

transitions is applied to the atom, it rapidly falls into the state |↓

and no longer interacts with the light field, allowing theinitialization of the qubit with essentially 100% fidelity (see e.g.,FIG. 1B).

When a single laser beam resonant with the |↑

↔|e

transition is applied, a closed cycling optical transition causes an ionin the |↑

state to fluoresce strongly while an ion in the |↓

state stays dark because the laser frequency is far from its resonance(see e.g., FIG. 1C). The collection of even a small fraction of thisfluorescence allows for the detection of the atomic qubit state withnear-perfect efficiency or accuracy. Other atomic species may havesimilar initialization/detection schemes.

In FIGS. 1B and 1C, all allowed transitions from the excited electronicstates |e

and |e′

140 are illustrated as downward, wavy arrows. On the other hand, theapplied laser radiation (which is shown as upward, straight arrows)drive these transitions for initialization to state |↓

as shown in FIG. 1B, and for fluorescence detection of the qubit state(|↑

=fluorescence, |↑

=no fluorescence) as shown in FIG. 1C.

This disclosure describes various aspects of techniques for fluorescenceimaging of trapped ion lattices. When detecting the fluorescence ofmultiple trapped ions in a stable lattice line configuration (e.g., thelattice 110 in FIG. 1A), it is important to distinguish each ion inposition, not only to diagnose the structure of the lattice, but also todetect individual qubits. The potential for crosstalk between thevarious ions can give rise to errors in the effective detection ofqubits. Moreover, in densely-packed lattices with imperfect opticalimages captured from the fluorescence, it may be challenging tounambiguously identify each atomic ion in the lattice. These challengesmay be mitigated based on the techniques described below.

For any of these techniques, a standard CCD camera (or other similarcamera, imaging device, or imager such as a CMOS-based imager), somewith a photomultiplier (PMT) gain, others with semiconductor gain. Forany camera, the readout speed is a critical parameter that can limit thedata rate of formation of the ion lattice image, and also the readouttime for a register of qubits. Examples of CCD cameras that may be usedinclude CCD cameras from Princeton Electronics and Andor.

The various imaging systems and techniques described in this disclosurecan be used for both the rapid identification of atomic ions during theloading of the ions into a trap and also for the high-efficiencydetection of the qubit state of each atomic ion, for example at theconclusion of a quantum computation. For typical applications, it isimportant to know exactly how many ions are confined in the trap. Thevarious imaging systems and techniques described in this disclosureallow for the real-time determination of ion number for loading and forqubit state detection.

Ion traps are generally loaded with ions that are photoionized from aneutral atomic beam flux. The ions can appear suddenly (e.g., in agroup), or also can appear one-by-one with appropriate control of thephotoionization laser intensity. Once the ions are loaded, laser coolingimmediately localizes them to the nanometer-scale in space (e.g., to afixed or quasi-fixed location), and they behave as effectivepoint-source optical objects. By having a trapped ion fluoresce and thenimaging of the fluorescence onto a camera (e.g., a 2D camera), it ispossible to perform an identification (e.g., location, number, and/orstate) of the trapped ion. In an alternative approach, trapped ions canbe loaded from a complex multi-zone trap whereby individual trapped ionsare shuttled from a previously-loaded trap zone to an experimental oroperational zone where the fluorescence is collected or detected.

The positions of the individual atoms may be determined by fitting theoverall intensity distribution to a sum of a variable number of Gaussianfunctions. It is then possible to determine the peak positions bycalculating the “Laplacian of Gaussians” (LoG) algorithm whose zeroesindicate the inflection point of the intensity distribution for eachpeak. In the LoG algorithm, the Laplacian is a 2D isotropic measure ofthe second spatial derivative of an image. The Laplacian of an imagehighlights regions of rapid intensity change and is therefore often usedfor edge detection. The Laplacian is often applied to an image that hasfirst been smoothed with something approximating a Gaussian smoothingfilter in order to reduce its sensitivity to noise. The LoG algorithmmay also be referred to simply as Laplacian or Marr filter. A“Difference of Gaussians” (DoG) algorithm may be used instead to quicklyapproximate the Laplacian, as subtracting two Gaussians with differentwidths essentially produces a second derivative. The DoG is an algorithmthat involves the subtraction of one blurred version of an originalimage from a less blurred version of the original image. In the case ofgrayscale images, the blurred images are produced by the convolution ofthe original grayscale images with Gaussian kernels having differingstandard deviations to suppress high-frequency spatial information. TheDoG algorithm is therefore a bandpass filter that removes spatialfrequencies from the original grayscale image. The technique describedabove, whether using the LoG algorithm or DoG algorithm, returns regionsof local maxima, which are easy to find even if the individual atomicimages overlap strongly.

The expected relative positions of atomic ions in an ion trap may beestimated and used as an initial best-guess for the ion positions, givenonly the number of ions in the trap. There are many differentconformations for the atomic ions in a lattice, from the simplest 1Dline, to 2D zig-zag lattices and then evolving to a series of 3D helicalstructures. The relative spacing of atomic ions in these configurationsmay be computed or determined for lattices of up to 100 or more atomicions. For insufficient anisotropy in the confinement, many atomic ionswill form 3D conformations and exhibit out-of-focus images, which maynot allow unambiguous identification of individual atoms. However, for1D (e.g., line lattices) and 2D (e.g., zig-zag or jagged lattices)structures, the identification of individual atomic ions should workwell, and even some simple 3D structures can be accuratelycharacterized.

FIG. 2A and FIG. 2B show representations of images 200 and 210,respectively, that illustrate examples of identification and labeling offluorescing atomic ions using imaging techniques in accordance with anaspect of the disclosure. Image 200 in FIG. 2A shows 67 trapped ¹⁷¹Yb⁺ions held in 3D (likely helical) structure. The image processingalgorithms described in this disclosure are used to identify each atomicion by its characteristic narrow intensity (fluorescing) pattern, andmay also label each ion with a sequential number by, for example,providing a circle and number for each identified ion—see e.g. FIG. 2C).In the image 200, a few of the ions are shown numbered for illustration,however, the full set of ions can be identified and numbered. Image 210in FIG. 2B shows 65 trapped ¹⁷¹Yb⁺ ions, held in a 2D zig-zag pattern,very close to the phase transition where the atoms form a 1D lattice.Again, as in FIG. 2A, the atomic ions are identified and labeled bynumber. In the image 210, a few of the ions are shown numbered forillustration, however, the full set of ions can be identified andnumbered. FIG. 2C shows a diagram 220 in which an example of theinformation displayed by the image processing algorithms described inthis disclosure for the identification and labeling of an atomic ion orsimilar qubit structure. In this example, the fluorescent pattern 225 ofa single atomic ion is identified by placing a circle 230 around it.Although a circle is used in this example, other geometric figures mayalso be used (e.g., squares, triangles, rectangles). Moreover, theatomic ion is also labeled and/or numbered (e.g., #25) by a textidentifier 235 (as also illustrated in the images 200 and 210). Thisallows a user and a quantum system to control and track trapped atomicions. The positioning and/or color of the text identifier 235 relativeto the fluorescent pattern 225, and the size, positioning, and/or colorof the circle 230 also relative to the fluorescent pattern 225, may beconfigured based on operational preferences. Characteristics of thecircle 230 and/or the text identifier 235 may vary within a same latticeto denote or identify certain subsets of atomic ions.

There may be instances in which a subset of atomic ions do not fluorescein the image, but their positions are made obvious by the larger gaps inthe lattice (e.g., gaps or spaces between fluorescent atomic ions asseen on the left side of FIG. 2B between ions #11 and #13). These “dark”ions may be caused from different atomic species that do not respond tothe laser light, or from ions of the nominal atomic species that aretrapped in a non-fluorescing state. These “dark” ions are oftentransient, blinking on and off, and their positions, bright or dark, areto be recorded and tracked by the image processing algorithms to keep anaccurate count of ions and to account for their positions.

For rapid loading of ions and real-time analysis of the number of ions,the image processing algorithms described above may be used to processthe intensity distribution, while controlling the intensity of thedriving laser that photoionizes the neutral atoms. In this way, it ispossible to control the number of atoms loaded into the trap and producea targeted number of trapped atomic ions.

As described above, another area in which the image processingalgorithms described in this disclosure apply is in high-efficiencysimultaneous detection of qubit states in a lattice of atomic ions. Inquantum information applications, trapped atomic ion qubits need to bemeasured through the collection of state-dependent fluorescence. Thisrequires the ability to discriminate between the spatially resolvedfluorescence patterns on the image, while minimizing crosstalk betweenadjacent atomic ions. Several new techniques in image recognition aredescribed to extract these qubit measurement results on largecollections of trapped ions.

Each individual atomic ion in the chain (e.g., in the chain or sequenceof atomic ions of the linear lattice 100) is prepared in a “bright”(fluorescence) qubit state, with all the other atomic ions prepared in a“dark” qubit state (no fluorescence), and the single fluorescing ionimage is collected. This procedure is similarly performed on all atomicions in the chain or lattice. Alternatively, a single ion may betranslated (e.g., moved) in position to known nominal positions of eachatomic ion in a large collection. In this way, the basis intensitydistribution is established for each atomic ion.

With any number of ions in a “bright” qubit state (or even all of themin a “bright” qubit state), the centroids and widths of each individualatomic ion intensity (e.g., fluorescent brightness) peaks are determinedas described above. This information is used to define a roughregion-of-interest (ROI) for each ion, which is a set of non-overlappingcircles. That is, the fluorescent pattern 225 shown in FIG. 2C isanalyzed to determine its centroid and/or width in order to identify itand assign it the appropriate labeling (e.g., the circle 230 and/or thetext identifier 235). These circles encompass CCD pixels that areassociated with a particular ion image. This allows a lowest-orderapproximation to the binary measurement (“bright” or “dark”) of each ionimage.

A maximum likelihood method is used to decompose the general measuredintensity distribution into the best fit linear combination of all Nbasis intensity distributions. From this inversion, the measured qubitvalue for each atomic ion may be determined.

In some implementations, the information from each individual CCD pixelmay be used to gain even lower crosstalk errors.

Periodically, all atomic ions may be made to fluoresce brightly, with ared-detuned near-resonant laser that Doppler cools the ions and keepsthem fluorescing strongly. The resulting image is used to adaptivelycorrect for slow movements of the atomic ion positions (e.g., to correctdrift over time), dropouts from losing atomic ions, and for compensationof drifts affecting crosstalk and scattered background light. Thiscalibration data may be collected with a low duty cycle (e.g., periodsof collections need not be too short) in order to not significantlyaffect the experimental data collection rate.

Referring now to FIG. 3, illustrated is an example computer device 300in accordance with aspects of the disclosure. The computer device 300can represent a single computing device, multiple computing devices, ora distributed computing system, for example. The computer device 300 maybe configured as a quantum computer, a classical computer, or acombination of quantum and classical computing functions. For example,the computer device 300 may be used to process information using quantumalgorithms based on trapped ion technology and may therefore implementadaptive and optimal imaging of individual trapped atomic ions within alattice for quantum computing. A generic example of a quantuminformation processing (QIP) system that can implement the imageprocessing algorithms of this disclosure for adaptive and optimalimaging of individual trapped atomic ions is illustrated in an exampleshown in FIGS. 6A and 6B.

In one example, the computer device 300 may include a processor 310 forcarrying out processing functions associated with one or more of thefeatures described herein. The processor 310 may include a single ormultiple set of processors or multi-core processors. Moreover, theprocessor 310 may be implemented as an integrated processing systemand/or a distributed processing system. The processor 310 may include acentral processing unit (CPU), a quantum processing unit (QPU), agraphics processing unit (GPU), or combination of those types ofprocessors. In one aspect, the processor 310 may refer to a generalprocessor of the computer device 300, which may also include additionalprocessors 310 to perform more specific functions such as imageprocessing algorithms.

In an example, the computer device 300 may include a memory 320 forstoring instructions executable by the processor 310 for carrying outthe functions described herein. In an implementation, for example, thememory 320 may correspond to a computer-readable storage medium thatstores code or instructions to perform one or more of the functions oroperations described herein. In one example, the memory 320 may includeinstructions to perform aspects of a methods 400 a and 400 b describedbelow in connection with FIGS. 4A and 4B, and a method 500 describedbelow in connection with FIG. 5. Just like the processor 310, the memory320 may refer to a general memory of the computer device 300, which mayalso include additional memories 320 to store instructions and/or datafor more specific functions such as instructions and/or data for imageprocessing algorithms.

Further, the computer device 300 may include a communications component330 that provides for establishing and maintaining communications withone or more parties utilizing hardware, software, and services asdescribed herein. The communications component 330 may carrycommunications between components on the computer device 300, as well asbetween the computer device 300 and external devices, such as deviceslocated across a communications network and/or devices serially orlocally connected to computer device 300. For example, thecommunications component 330 may include one or more buses, and mayfurther include transmit chain components and receive chain componentsassociated with a transmitter and receiver, respectively, operable forinterfacing with external devices.

Additionally, the computer device 300 may include a data store 340,which can be any suitable combination of hardware and/or software, thatprovides for mass storage of information, databases, and programsemployed in connection with implementations described herein. Forexample, the data store 340 may be a data repository for operatingsystem 360 (e.g., classical OS, or quantum OS). In one implementation,the data store 340 may include the memory 320.

The computer device 300 may also include a user interface component 350operable to receive inputs from a user of the computer device 300 andfurther operable to generate outputs for presentation to the user or toprovide to a different system (directly or indirectly). The userinterface component 350 may include one or more input devices, includingbut not limited to a keyboard, a number pad, a mouse, a touch-sensitivedisplay, a digitizer, a navigation key, a function key, a microphone, avoice recognition component, any other mechanism capable of receiving aninput from a user, or any combination thereof. Further, the userinterface component 350 may include one or more output devices,including but not limited to a display, a speaker, a haptic feedbackmechanism, a printer, any other mechanism capable of presenting anoutput to a user, or any combination thereof.

In an implementation, the user interface component 350 may transmitand/or receive messages corresponding to the operation of the operatingsystem 360. In addition, the processor 310 may execute the operatingsystem 360 and/or applications or programs, and the memory 320 or thedata store 340 may store them.

When the computer device 300 is implemented as part of a cloud-basedinfrastructure solution, the user interface component 350 may be used toallow a user of the cloud-based infrastructure solution to remotelyinteract with the computer device 300.

FIG. 4 is a flow diagram that illustrates an example of a method 400 afor identification of optically active systems including one or moreindividual atoms in accordance with aspects of this disclosure. Themethod 400 a is applicable to different optically-active systems oroptically-active qubits in addition to individual atoms or individualions. For example, the method 400 a is also applicable to differenttypes of quantum emitters (also referred simply as emitters) includingsolid-state quantum emitters such as quantum dots. In an aspect, themethod 400 a may be performed in a computer system such as the computerdevice 300 described above, where, for example, the processor 310, thememory 320, the data store 340, and/or the operating system 360 may beused to perform the functions of the method 400 a. Similarly, thefunctions of the method 400 a may be performed by one or more componentsof a QIP system such as a QIP system 600 as shown in FIG. 6A and itscomponents (e.g., imaging system 640 and its subcomponents shown in FIG.6B).

At 410, the method 400 a includes providing an optical source thatproduces fluorescence from the quantum emitters as they are loaded intoa trap, each of the quantum emitters behaving as an optical objecthaving a certain intensity distribution in response to the fluorescence.In an aspect, a trap may refer to an atom trap or an ion trap, and maybe used to confine and arrange quantum emitters such as atoms or ions.

At 420, the method 400 a includes identifying a position of each of thequantum emitters by fitting the overall intensity distribution to a sumof a variable number of Gaussian functions.

At 430, the method 400 a includes controlling, in real-time, a number ofquantum emitters that are loaded into the trap based at least on theidentified positions of each of the quantum emitters and whether one ormore of the quantum emitters are not fluorescing.

In an aspect of the method 400 a, the method may further includegenerating a flux of neutral quantum emitters including the quantumemitters, and controlling an intensity of a laser that photoionizes theneutral quantum emitters to produce ionized quantum emitters (e.g.,ionizing atoms into ions).

In an aspect of the method 400 a, identifying the position of each ofthe quantum emitters includes fitting the overall intensity distributionto the sum of a variable number of Gaussian functions comprisesdetermining peak positions in the overall distribution of intensities bycalculating the Laplacian of Gaussians whose zeros indicate aninflection point of the intensity distribution for each peak. Adifference of Gaussians algorithm may be used to approximate theLaplacian of Gaussians.

In an aspect of the method 400 a, identifying the position of each ofthe quantum emitters may include fitting the overall intensitydistribution to the sum of a variable number of Gaussian functions toproduce regions of local maxima corresponding to a centroid in positionof each quantum emitter.

In another aspect of the method 400 a, identifying the position of eachof the quantum emitters by fitting the overall intensity distribution tothe sum of a variable number of Gaussian functions includes determininga relative spacing between quantum emitters based on a number of quantumemitters to be trapped in the trap, determining an initial andapproximate position of each of the quantum emitters based on therelative spacing, and identifying the position of each of the quantumemitters based at least in part of the initial and approximate positionof each of the quantum emitters. The relative spacing may be computedfor a lattice of up to 10 quantum emitters, up to 20 quantum emitters,up to 30 quantum emitters, up to 40 quantum emitters, up to 50 quantumemitters, up to 60 quantum emitters, up to 70 quantum emitters, up to 80quantum emitters, up to 90 quantum emitters, or up to 100 quantumemitters. In some instances the relative spacing may be computed forlattices with more than 100 quantum emitters such as up to 110 quantumemitters, up to 120 quantum emitters, or more. Moreover, the relativespacing may be computed for a lattice having a one-dimensional (1D)conformation, a two-dimensional (2D) conformation, or athree-dimensional (3D) conformation of the atomic ions.

In yet another aspect of the method 400 a, the method may furtherinclude performing a corrective action when the number of quantumemitters that are loaded into the trap is not the correct number, whenan incorrect quantum emitter species is loaded into the trap, or whenone or more of the quantum emitters loaded into the trap cannot beproperly initialized.

FIG. 4B is a flow diagram that illustrates an example of a method 400 bfor identification of optically active systems including one or moreindividual quantum emitters in accordance with aspects of thisdisclosure. The method 400 b is applicable to different optically-activesystems or optically-active qubits including different types of quantumemitters (e.g., atoms, ions, or solid-state quantum emitters such asquantum dots). In an aspect, the method 400 b may be performed in acomputer system such as the computer system 300 described above, where,for example, the processor 310, the memory 320, the data store 340,and/or the operating system 360 may be used to perform the functions ofthe method 400 a. Similarly, the functions of the method 400 b may beperformed by one or more components of a QIP system such as a QIP system600 as shown in FIG. 6A and its components (e.g., imaging system 640 andits subcomponents shown in FIG. 6B).

At 440, the method 400 b includes providing an optical source thatproduces fluorescence from the quantum emitters within an optical fieldof view, each of the quantum emitters behaving as an optical objecthaving a certain intensity distribution in response to the fluorescence.

At 450, the method 400 b includes identifying a position of each of thequantum emitters by fitting the overall intensity distribution to a sumof a variable number of Gaussian functions.

At 460, the method 400 b includes controlling, in real-time, a number ofquantum emitters that are within the optical field of view based atleast on the identified positions of each of the quantum emitters andwhether one or more of the quantum emitters are not fluorescing.

FIG. 5 is a flow diagram that illustrates an example of a method 500 foridentification of atom quantum systems to detect the qubit states inaccordance with aspects of this disclosure. In an aspect, the method 500may be performed in a computer system such as the computer system 300described above, where, for example, the processor 310, the memory 320,the data store 340, and/or the operating system 360 may be used toperform the functions of the method 500. Similarly, the functions of themethod 500 may be performed by one or more components of a QIP systemsuch as the QIP system 600 and its components (e.g., imaging system 640and its subcomponents).

At 510, the method 500 includes preparing, for each of multiple trappedquantum emitters a particular quantum state that fluoresces whilekeeping all other quantum emitters in a different quantum state toestablish an individual basis intensity distribution for each of themultiple trapped quantum emitters. The quantum state or qubit state thatfluoresces may be referred to as a bright state or bright qubit state,while the different quantum states or qubit states may be referred to asdark states or dark qubit states.

At 520, the method 500 includes determining a peak intensity for themultiple trapped quantum emitters in a bright qubit state.

At 530, the method 500 includes performing a maximum likelihood methodto decompose a distribution of the peak intensities into a best fitlinear combination of all of the individual basis intensitydistributions.

At 540, the method 500 includes identifying a qubit value for themultiple trapped quantum emitters based on results from the maximumlikelihood method decomposition.

In an aspect of the method 500, preparing the bright qubit state foreach of the multiple trapped quantum emitters and determining the peakintensity includes providing an optical source that producesfluorescence of a respective trapped quantum emitter. The optical sourcemay be a red-detuned near-resonant laser that Doppler cools the multipletrapped quantum emitters and keeps the fluorescing strongly.

In an aspect of the method 500, identifying the qubit value includesadaptively correcting the multiple trapped quantum emitters in responseto the maximum likelihood method decomposition indicating that there isa slow movement of at least a subset of the multiple trapped quantumemitters, there are dropouts from losing trapped quantum emitters, ordrifts affecting crosstalk and scattered background light needcompensation.

In another aspect of the method 500, determining the peak intensity forthe multiple trapped quantum emitters in the bright qubit state includesidentifying a region-of-interest (ROI) for each trapped atomic ion basedon one or more of a centroid or a width of a respective individual peakintensity. The ROI may be labeled with one or more of a text identifieror a geometric shape encircling the respective individual peakintensity. In an example, the text identifier can indicate a numberassigned to the respective trapped quantum emitter. However, othertextual indicators such as combinations of letters, numbers, symbols, orthe like can also be used.

In another aspect of the method 500, identifying of the qubit value forthe multiple trapped quantum emitters may be performed periodically aspart of a calibration procedure. In such cases, the period may beselected to be long so as to not affect regular operation.

FIG. 6A is a block diagram that illustrates an example of a QIP system600 in accordance with aspects of this disclosure. The QIP system 600may also be referred to as a quantum computing system, a computerdevice, or the like. In an aspect, the QIP system 600 may correspond toportions of a quantum computer implementation of the computer device 300in FIG. 3.

The QIP system 600 can include a source 660 that provides atomic species(e.g., a flux of neutral atoms) to a chamber 650 having an ion trap 670that traps the atomic species once ionized (e.g., photoionized) by anoptical controller 620. The chamber 650 may be an example of the vacuumchamber 100 in FIG. 1A. In another aspect, the ion trap 670 may bereferred to as a trap, a surface trap, an atom trap, or an atomiclattice that may be configured to trap or confine different atomicspecies. Optical sources 630 in the optical controller 620 may includeone or more laser sources that can be used for ionization of the atomicspecies, control (e.g., phase control) of the atomic ions, and forfluorescence of the atomic ions that can be monitored and tracked byimage processing algorithms operating in an imaging system 640 in theoptical controller 620. The imaging system 640 (see e.g., FIG. 6A) caninclude a high resolution imager (e.g., CCD camera) for monitoring thequantum emitters while they are being provided to the ion trap 670(e.g., for counting) as described in connection with method 400 a orafter they have been provided to the ion trap 670 (e.g., for monitoringthe atomic ions states) as described in connection with method 500, orfor monitoring or identifying solid-state quantum emitters as describedin the method 400 b. In an aspect, the imaging system 640 can beimplemented separate from the optical controller 620, however, the useof fluorescence to detect, identify, and label atomic ions using imageprocessing algorithms may need to be coordinated with the opticalcontroller 620.

The QIP system 600 may also include an algorithms component 610 that mayoperate with other parts of the QIP system 600 (not shown) to performquantum algorithms or quantum operations. As such, the algorithmscomponent 610 may provide instructions to various components of the QIPsystem 600 (e.g., to the optical controller 620) to enable theimplementation of the quantum algorithms or quantum operations.

FIG. 6B shows the imaging system 640 of FIG. 6A in more detail bydescribing examples of subcomponents that may be part of the imagingsystem 640. In an example, the imaging system 640 may include aprocessor 641, a memory 642, an imager 643 (e.g., a CCD camera or a CMOScamera), a loading identification component 644, and a qubit statedetection algorithms component 645. The processor 641 and the memory 642may operate together to perform or execute the functions of the imagingsystem 640 and/or to control the various subcomponents of the imagingsystem 640. The imager 643 may be used to capture the fluorescence frombright atomic ions or quantum emitters as described above. The loadingidentification component 644 may be implemented in hardware and/orsoftware and is configured to perform various aspects described hereinrelated to the rapid identification of quantum emitters (e.g., atoms)during loading, including aspects described above in connection with themethod 400 a. Similarly, the qubit state detection algorithms component645 may be implemented in hardware and/or software and is configured toperform various aspects described herein related to high efficiencysimultaneous detection of qubit states in a lattice of atomic ions or afield of view with quantum emitters, including aspects described abovein connection with the method 500. In some implementations, at leastsome of the functions or operations performed by the loadingidentification component 644 and/or the qubit state detection algorithmscomponent 645 may be performed by the processor 641 based oninstructions, data, or both in the memory 642.

Although the present disclosure has been provided in accordance with theimplementations shown, one of ordinary skill in the art will readilyrecognize that there could be variations to the embodiments and thosevariations would be within the scope of the present disclosure.Accordingly, many modifications may be made by one of ordinary skill inthe art without departing from the scope of the appended claims.

What is claimed is:
 1. A method for identification of quantum emitters,comprising: preparing, for each of multiple trapped quantum emitters, aparticular quantum state that fluoresces while keeping all other quantumemitters in a different quantum state to establish individual basisintensity distributions for each of the multiple trapped quantumemitters; determining a peak intensity for the multiple trapped quantumemitters in a particular quantum state that fluoresces; performing amaximum likelihood method to decompose a distribution of the peakintensities into a best fit linear combination of all of the individualbasis intensity distributions; and identifying a qubit value for themultiple trapped quantum emitters based on the best fit linearcombination.
 2. The method of claim 1, wherein the particular quantumstate that fluoresces includes a bright state or bright quantum state,and wherein the different quantum state includes a dark state or darkquantum state.
 3. The method of claim 2, wherein preparing the brightquantum state for each of the multiple trapped quantum emitters anddetermining the peak intensity includes providing an optical source thatproduces fluorescence of a respective trapped quantum emitter.
 4. Themethod of claim 3, wherein the optical source is a red-detunednear-resonant laser that Doppler cools the multiple trapped quantumemitters and keeps the fluorescing strongly.
 5. The method of claim 1,wherein identifying the qubit value comprises adaptively correcting themultiple trapped quantum emitters in response to the best fit linearcombination indicating that there is a slow movement of at least asubset of the multiple trapped quantum emitters, there are dropouts fromlosing trapped quantum emitters, or drifts affecting crosstalk andscattered background light need compensation.
 6. The method of claim 3,wherein determining the peak intensity for the multiple trapped quantumemitters in the bright quantum state comprises identifying aregion-of-interest (ROI) for each trapped quantum emitter based on oneor more of a centroid or a width of a respective individual peakintensity.
 7. The method of claim 6, further comprising labeling the ROIwith one or more of a text identifier or a geometric shape encirclingthe respective individual peak intensity.
 8. The method of claim 7,wherein the text identifier can indicate a number assigned to therespective trapped quantum emitter.
 9. The method of claim 1, whereinthe identifying of the qubit value for the multiple trapped quantumemitters is performed periodically as part of a calibration procedure.10. The method of claim 1, further comprising trapping the multipletrapped quantum emitters in a trap of a quantum information processing(QIP) system.
 11. A quantum information processing (QIP) system,comprising: a quantum emitter lattice; an optical controller configuredto prepare, for each of multiple quantum emitters trapped in thelattice, a bright qubit state while keeping all other quantum emittersin a dark qubit state to establish an individual basis intensitydistribution for each of the multiple trapped quantum emitters; and animaging system configured to: determine a peak intensity for each of themultiple trapped quantum emitters in the respective bright qubit state;perform a maximum likelihood method to decompose a distribution of thepeak intensities into a best fit linear combination of all of theindividual basis intensity distributions; and identify a qubit value forthe multiple trapped quantum emitters based on the best fit linearcombination.
 12. The QIP system of claim 11, wherein each of the quantumemitters is an atom, an ion, or a solid-state quantum emitter.
 13. TheQIP system of claim 11, wherein the imaging system includes a CCD cameraor a CMOS camera.
 14. The QIP system of claim 11, further comprising atrap configured to form the quantum emitter lattice.
 15. The QIP systemof claim 11, further comprising an optical source configured to producefluorescence of a respective trapped quantum emitter to determine thepeak intensity.
 16. The QIP system of claim 15, wherein the opticalsource is a red-detuned near-resonant laser configured to Doppler coolthe multiple trapped quantum emitters.
 17. A non-transitorycomputer-readable medium storing code with instructions executable by aprocessor for identification of quantum emitters, comprising: code forpreparing, for each of multiple trapped quantum emitters, a bright qubitstate while keeping all other quantum emitters in a dark qubit state toestablish an individual basis intensity distribution for each of themultiple trapped quantum emitters; code for determining a peak intensityfor each of the multiple trapped quantum emitters in the respectivebright qubit state; code for performing a maximum likelihood method todecompose a distribution of the peak intensities into a best fit linearcombination of all of the individual basis intensity distributions; andcode for identifying a qubit value for the multiple trapped quantumemitters based on the best fit linear combination.
 18. Thenon-transitory computer-readable medium of claim 17, wherein the codefor identifying the qubit value comprises code for adaptively correctingthe multiple trapped quantum emitters in response to the best fit linearcombination indicating that there is a slow movement of at least asubset of the multiple trapped quantum emitters, there are dropouts fromlosing trapped quantum emitters, or drifts affecting crosstalk andscattered background light need compensation.
 19. The non-transitorycomputer-readable medium of claim 17, wherein the code for determiningthe peak intensity for the multiple trapped quantum emitters in thebright qubit state comprises code for identifying a region-of-interest(ROI) for each trapped quantum emitter based on one or more of acentroid or a width of a respective individual peak intensity.
 20. Thenon-transitory computer-readable medium of claim 17, wherein the codefor identifying the qubit value for the multiple trapped quantumemitters is performed periodically as part of a calibration procedure.